Abstract

In this presentation, we consider artificial neural networks for inverse scattering problems. As aworking model, we consider the inverse problem of recovering a scattering object from the (possibly) limited-aperture radar cross section (RCS) data collected corresponding to a single incident field. This nonlinear and ill-posed inverse problem is practically important and highly challenging due to the severe lack of information. From a geometrical and physical point of view, the low-frequency data should be able to resolve the uniqueidentifiability issue, but meanwhile lose the resolution. On the other hand, the machine learning can be used to break through the resolution limit. By combining the two perspectives, we develop a fully connected neural network (FCNN) for the inverse problem. Extensive numerical results show that the proposed method can produce stunning reconstructions. The proposed strategy can be extended to tackling other inverse scattering problems with limited measurement information.

Short bio

张凯教授 2006 年获吉林大学博士学位，2008 年获得香港中文大学联合培养博士学位。 2008-2010 年，赴密歇根州立大学开展博士后研究。张凯教授先后赴伊利诺伊州立大学， 奥本大学，香港浸会大学，南方科技大学等开展合作研究，研究兴趣为随机偏微分方程的 数值解法。主要从事随机麦克斯韦方程和随机声波方程数值方法，期权定价和套利的数值 方法研究。先后主持国家自然科学基金等项目 11 项，发表论文 50 余篇。